cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929383539736576 |
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| author | Sundar, Anirudh Xu, Jin Gay, William Richardson, Christopher Heck, Larry |
| author_facet | Sundar, Anirudh Xu, Jin Gay, William Richardson, Christopher Heck, Larry |
| contents | An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be developed specifically for each component to support the depth of inquiry and interactions required by research scientists. This work introduces Conversational Papers (cPAPERS), a dataset of conversational question-answer pairs from reviews of academic papers grounded in these paper components and their associated references from scientific documents available on arXiv. We present a data collection strategy to collect these question-answer pairs from OpenReview and associate them with contextual information from LaTeX source files. Additionally, we present a series of baseline approaches utilizing Large Language Models (LLMs) in both zero-shot and fine-tuned configurations to address the cPAPERS dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers Sundar, Anirudh Xu, Jin Gay, William Richardson, Christopher Heck, Larry Computation and Language Artificial Intelligence Machine Learning An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be developed specifically for each component to support the depth of inquiry and interactions required by research scientists. This work introduces Conversational Papers (cPAPERS), a dataset of conversational question-answer pairs from reviews of academic papers grounded in these paper components and their associated references from scientific documents available on arXiv. We present a data collection strategy to collect these question-answer pairs from OpenReview and associate them with contextual information from LaTeX source files. Additionally, we present a series of baseline approaches utilizing Large Language Models (LLMs) in both zero-shot and fine-tuned configurations to address the cPAPERS dataset. |
| title | cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.08398 |